Carrying out research and development on usage search engines is an interdisciplinary effort that requires expertise from information retrieval, information visualization and interface design, as well as domain knowledge from computer linguistics.

we divide our review of related work into four parts: (1) existing search engines and web services, (2) retrieval engines and wildcard search from the perspective of information retrieval, (3) search result visualization, and, (4) writing support systems dedicated to second language writers.


We consider this fact by applying tail pruning for postlists that are too long to be read at once into main memory.

less frequent n-grams that might match a given query can be missed.


This paper addresses the issue of a fine-grained NER of 50 classes for Arabic and presents a comprehensive set of experiments that evaluate innovative means of representing the features set.

the contribution of this paper falls into different categories with unique outcomes, as follows: One of the earliest corpora publically released was ANERcorp, developed by .


Two approaches have been presented that rely on the output of the dependency parser and the clustering algorithm, instead of on a local window-based representation.

far, this study has examined the window-based and dependency-based representation of evidence, in order to increase the performance of the classification process.


Such languages are collectively spoken by billions, yet remain seriously under-resourced.

while our focus is on endangered languages, the approach applies to under-resourced languages in general.


The lack of proximal mains electricity meant that it was not possible to run the router and synchronise the data on each phone.

of this (and participation issues discussed below) the researcher only kept two devices in use at a time, making it easier to keep track of what was on each device.


Relationships beyond this are negotiated through extended familial relations of reciprocity.

there were no opportunities to arrange community meetings as in Terra Preta, or even to find an individual who was an officially designated leader.


Therefore, there were no opportunities to arrange community meetings as in Terra Preta, or even to find an individual who was an officially designated leader.

much time was spent engaging a small number of enthusiastic participants and working with them to engage other members of the community through existing social networks.


On many occasions, people listened to a traditional narrative and then asked to recount their own version.

we see the possibility for achieving substantial lexical overlap in recordings by different speakers, which could help with speech modelling, dialect identification, and lexicon production.


These statistics have been arrived at based on two fundamental assumptions: (a) adpositions constitute a large fraction of function words; and (b) the strict ordering between the adposition and the noun phrase leads to differential co-occurrence characteristics on the left and right sides of the adposition.

if the function words of a language are automatically detected and the co-occurrence statistics on the left and right of those words are appropriately analyzed, then it should be possible to tell the prepositional languages apart from the postpositional ones.


English and Italian are prepositional languages with subject-verb-object word order, while Hindi and Bangla are postpositional, relatively free word order with preference for subject-object-verb.

any function word characterization strategy that works across these languages is expected to work for a large variety of languages.


For example, even though the and in will be very frequent in most English corpora, meanwhile and off may not be so.

if frequency is used as a function word detector with small datasets, we will have a problem of low recall.


We hypothesize that even if a content word, e.g., government, might have high frequency owing to the nature of the domain, there will only be a relatively fewer number of words that can co-occur immediately after or before it.

the co-occurrence count may be a more robust indicator of function words.


If the LCE or LCC of a word w is high, it means that a large number of distinct words can precede w in the language (additionally, almost with equal probabilities for high LCE).

predicting the previous word of w is difficult.


In fact, the philosophy of this research is to be of assistance in these less-known cases.

co-occurrence statistics have potential in predicting the adposition typology of a new language, which we leverage in this research.


The average number of characters in words of these languages were found to be in the relatively higher range of nine to eleven.

function words, especially the adpositions, seldom occur as free words in these languages and hence our method cannot capture the distributional characteristics of the adpositions.


Already 2-year-olds are able to form new words by using compounds consisting of two morphemes (Clark, 1981).

compounds are a very common word type but many occur with a very low token count.


What is important for AdvD of the v podarok type is the role of the actant with respect to giving.

in (21b) Ivan is the Recipient and not the Agent of reward: (21b) Ivan potreboval sebe v nagradu poltsarstva 'Ivan demanded half of the kingdom as a reward'.


In the field of SLU, as well as other fields of natural language processing, gathering data is fairly cheap but labeling is quite expensive and time-consuming.

active learning methods apply very well and can greatly reduce costs.


In this study, based on the classifier proposed by Yoshinaga and Kitsuregawa (2009), a self-adaptive classifier that enumerates common classification problems from a given text stream and reuses their results is proposed.

the proposed classifier adaptively speeds up the classification of forthcoming classification problems.


As a result, not all effects of making modifications to a system are known without actually making the modification and generating translations.

in the process of developing an SMT system, it is common to evaluate actual translations to identify problems to make improvements.


In particular, for discriminative LMs, n-gram features that have large positive or negative weights indicate n-grams that are under-generated or over-generated by the system.

by examining high-weighted or low-weighted n-grams, it is likely that we will be able to get a grasp of the system mistakes.


Almost high-weighted n-grams are only contained in oracle translations, and not contained in 1-best translation.

we use low-weighted n-grams for evaluation.


Following Huang and Mi (2010), the hypotheses with same translation step 1 fall into the same bin.

only the prediction (pr) actions actually make a jump from a bin to another.


Noted that as we number the bins by the translation step, only pr actions make progress, the sc and co actions are treated as "closure" operators in practice.

we always do as many sc/co actions as possible immediately after a pr step until the symbol after the dot is another non-terminal.


The structured language model introduced in our work only takes into account the target string, and ignores the reordering information in the source side.

our future work seeks to incorporate more source side syntax information to guide the parsing of the target side, and tune a structured language model for both Bleu and paring accuracy.


The key reason is that most of annotated corpora are drawn from news texts.

the system trained on these corpora cannot work well with the out-of-domain texts.


Since these supervised approaches often has a high requirement on the quality and quantity of annotated corpus, which is always not easy to create.

many methods were proposed to utilize the information of unlabeled data.


If the entropy of the character is larger than threshold T se , we say that this character may be in an uncertain context.

we extract the consecutive uncertain characters and their contexts as the expansion seeds.


We do not use any search API and directly use the returned webpages by search engine, then extract the snippets and titles.

we just write a simple program to collect the webpages and clean them.


In our experiments, the number of selected sentence is 1 ∼ 5 for each seed.

we can re-train a new segmenter on the expanded corpus.


Examples are the suffix s marking tense and person in she writes and number in stations.

the suffix class is closed and has only a small number of morphemes compared to the prefix and stem categories.


Thus the suffix class is closed and has only a small number of morphemes compared to the prefix and stem categories.

a large coverage of suffixes can be achieved already with a relatively small annotated data set.


Their solution was to introduce corpus likelihood weights α and β, one for the unannotated data set and one for the annotated data set.

instead of optimizing the MAP estimate, Kohonen et al.


The above observations indicate that word reordering and dependency parsing depend on each other.

we consider it is more desirable to concurrently perform the two processings than to sequentially perform them.


On the other hand, it is well known that CYK algorithm can efficiently find the optimal dependency structure which satisfies Japanese syntactic constraints.

in our research, we have extended the CYK algorithm for the conventional dependency parsing so that it can find the suboptimal D and O from among the dependency structures and word order patterns which satisfy the conditions 1, 2 and 3.


As we can see from Figure 7(3) and Figure 7(4) , the ROUGE scores reach their peaks around 0.6 and then drop afterwards.

the topic number is set to 12 and a is set to 0.6 in the test dataset.


Our assumption is that authors somehow maintain their unique writeprints across different topics.

even though the additional data comes from a topic different than that of the test data, we expect to see improvements in the performance of cross-topic AA.


For each selection of four training topics, we averaged performance over three random samplings where in each sampling we randomly selected one document per author in each training topic.

we ended up with the same number of documents for training both models.


Our primary intuition is that the sketches provide a language-independent means of communication of concepts that is effectively employed for the generation of phrase equivalents.

we leverage a fun drawing-guessing game to fulfill the linguistic purpose of generating phrase-equivalents.


As the number of verbs in the simplex group is the largest among the three verb groups, chances are that the number of recognized texts in entailment relations using verbs in this group would be the largest among the three groups.

verbs in this group should be used to recognize as much relevant information as possible in spite of low precision.


However, as a PASCAL RTE dataset for information extraction is composed of pairs of texts, rather than a text and a structured template like the second type mentioned above (Dagan et al., 2009) , it was difficult to validate the proposed approach's capability of inferring verbs from nouns.

we decided to use pairs of templates and texts from Wikipedia because they are easily found in Wikipedia.


We also selected Advil, Aspirin, and Benadryl as a set of sample representative instances of the domain Drug.

queries d i || v f were built like "pasta makes from", "pasta made from" or "pasta is made from" for each combination of a domain instance and an inferred verb.


Editing Wikipedia articles naturally includes linking them to their related articles, which imposes an additional burden on contributors.

wikipedia articles may remain incomplete; they sometimes lack important links as well as contain incorrect links.


As a result, Wikipedia articles may remain incomplete; they sometimes lack important links as well as contain incorrect links.

it is desirable to automate linkrelated editorial tasks such as embedding links in new articles and verifying links in existing articles.


Both the positive word "nice" and the negative phrase "not attractive" are used literally.

it was necessary to filter out messages containing such connectives.


), the adverbs really and extremely are used to strengthen the ironic effect.

combinations of degree adverb phrases and a positive adjective are used as patterns to find possible irony expressions automatically in the candidate dataset.


In most cases, even when no proper contextual information is present, the listener can tell the literal meaning is not meant because it violates most people's inclinations.

the use of this pattern is usually nonliteral and ironic.


On the other hand, Ya-hoo blogs are not limited in length and a blog article itself is not part of the conversation.

the blog articles are usually more formal compared to microblog messages.


Although direct transfer methods are straightforward and easy to implement, they are vulnerable to missing or incorrect alignments which lead to missing and erroneous annotations in the target language.

non-literal translations and translation shifts present major problems for these methods.


The syntactic label set used for French (Candito et al., 2009) is less fine-grained than the English labels (20 versus 36).

the mapping from English syntactic labels to French treebank labels is for the most part a many-to-one mapping, which leads to information loss but suffices for our purpose as will be shown in the next section.


The semantic continuum makes it always possible to break a sense into finer grained subsenses.

the problem is one of finding the optimal granularity for any given task.


Although it is difficult to obtain labeled data, we can easily obtain sufficient unlabeled data.

in this paper, we study the task of product record linkage in a semi-supervised setting by leveraging both the learning ability of the classifiers and the usefulness of the large amount of unlabeled data.


the number of nonlinked pairs is much more than that of linked pairs.

we mainly study the confidence of a candidate positive instance.


We consider the following two sets: Intuitively, if we know r ∼ r , then all the pairs in both S r and S r must not be linked.

we define the conflicting set of pair (r, r ) as S r,r cf l = S r ∪ S r .


It would occur due to the presence of many noisy words and the unreasonably too large ratios between weights of different terms.

it could result in poor representations of sentiments containing in documents.


Let V 0 denote the manually labeled documents, if the document expresses positive sentiment (V 0 ) ii = 1, and (V 0 ) i2 = 1 for negative sentiment.

the semi-supervised learning with lexical knowledge can be written as: where Tr(•) denotes the trace of a matrix, α > 0 and β > 0 are the parameters which control the contribution of lexical prior knowledge and manually labeled documents.


Moreover, we introduce the geometric structures from both document and word sides as co-regularization.

our proposed unsupervised (or semi-supervised) sentiment classification framework can be mathematically formulated as solving the following optimization problem: where δ > 0 and γ > 0 are parameters which control the contributions of document space and word space geometric information, respectively.


Based on Lemmas 3.1 and Theorem 3.2, the update rule for U can be obtained by minimizing F(U ij By Lemma 3.1 and Theorem 3.2, we have L( where Iter denotes the number of iteration number.


Similarly, for , we also only need zero addition and mk multiplications for each of them.

besides, we also note that W u is a sparse matrix, if we use a p-nearest neighbor graph, the average nonzero elements on each row of W u is p. we only need mpk additions and mpk multiplications to compute W u U.


Suppose the multiplicative updates stop after Iter iterations, the time cost of multiplicative updates then becomes O(Iter × mnk).

the overall running time of GNMTF is similar to the standard NMTF and CNMTF.


Taking the sentence in Figure 1(a) as an example, because the word "多亏" (fortunately) is so chatty, 1 the parsing result is wrong.

are unable to extract the T-P collocation 键盘, 好 ( keyboard, good ).


However, their work only focused on English sentences, whereas the relations for Chinese sentences are different.

in accordance with Chinese grammar, we proposed nine syntactic structure rules between target t and polarity word p in a Chinese T-P collocation t, p .


As discussed above, the current parser often produces wrong parse trees because of these feeling words.

the feeling words tend to be removed from a Basic Features 01:  We can obtain a feeling word lexicon from HowNet, 5 a popular Chinese sentiment thesaurus, where a feeling word is defined by DEF={perception|感知} tag.


This method needs more resource and is more complicated.

in this paper, we only focus on extractive approach.


Sentences with virtual dependency nodes were omitted, as they are not annotated in the constituent treebank and their treatment in dependency trees is also problematic Seeker et al., 2012).

we worked with 7,372 sentences and 162,960 tokens.


As mentioned in Section 2, there is a limited number of idiosyncratic correspondences between elements of SSyntS and DSyntS (the correspondences (iv-vii) which can be straightforwardly handled by a rule-based postprocessor because (a) they are non-ambiguous, i.e., a ↔ b, c ↔ d ⇒ a = b ∧ c = d, and (b) they are few.

only determiners and auxiliaries in SSyntS map onto a grammeme in DSyntS, both SSyntS and DSyntS count with less than a dozen grammemes, etc.


Our interpretation is that the features we use for this task are appropriate, but that the training data set is too small.

some prepositions are erroneously left out from or introduced into the DSyntS.


Moreover, they provide flexibility in terms of learning composition weights based on supervised or unsupervised objectives.

rNN learn feature representations depending on the task.


We note that in general event composition is more difficult than traditional semantic composition since the former also deals with pragmatics.

the set of nonsensical events is different from the set of anomalous sentences, and while meaningless events and well composed normal events are two ends of the semantic spectrum, semantically valid anomalous events lie somewhere between them.


PPTT is different in that it primarily aims at deriving various types of semantic relations on a large scale exploiting the notion of the phase of story, rather than being a comprehensive taxonomy like those existing semantic lexicons.

ppTT can derive more varieties of semantic relations between templates than any one of those existing lexicons.


Some words, such as telephone and communication might even be rated as related because they belong to the same semantic field.

they proposed to split the WORDSIM353 dataset into two subsets: the first one comprising words that are similar, i.e., synonyms, antonyms and hyperonym-hyponym and the second cohyp hyper mero attri event randn randj randv 2 1 0 1 2 3 cohyp hyper mero attri event randn randj randv Figure 3: Similarity score distributions for various semantic relations on the BLESS dataset, without using the transition matrix (left) and with using the transition matrix (right) for comparing adjectives and verbs with nouns.


We follow the evaluation proposed by the authors: for each pair of concept-relation, we keep the score of the most similar relatum associated to that pair of concept-relation.

for each concept, we have eight scores, one for each relation.


The disfluencies in speech introduce noise in downstream tasks like machine translation and information extraction.

the task of disfluency detection not only can help improve the readability of automatically transcribed speech, but also the performance of downstream NLP tasks.


They are insertions in spontaneous speech that indicate pauses or mark boundaries in discourse.

they do not convey useful content information.


In general, edit words are more difficult to detect than filler words, and so edit word prediction accuracy is much lower.

in this work, we mainly focus on edit word detection.


Furthermore, good decision making relies on the quality of the arguments being presented and the process by which they are resolved.

it is not surprising that argumentation has long been a topic of interest in academic research, and different models have been proposed to capture the notion of an argument (Toulmin, 1958;Freeley and Steinberg, 2008).


Most importantly, as implied by its name, a CDC is defined with respect to a given context -the input Topic.

identifying general characteristics of a claim-like statement as done in (Mochales Palau and Moens, 2009) is not sufficient, since one should further identify the relevance of the candidate claim to the Topic.


The purpose of the funnel is to gradually focus on smaller and smaller CDC-containing text segments, while filtering out irrelevant text.

the cascade divides the high level CDCD problem into smaller and more tangible problems -given an article, detect sentences that include CDCs; given a sentence, detect the exact CDC boundaries; given a set of CDC candidates, rank them so that true candidates are on top.


Each article contains an average of 155 sentences, each sentence spans on average 23 words, i.e., ≈ 200 sub-sentences, each of which may represent a candidate CDC.

in principle, for each Topic we consider around 300,000 candidate CDCs, of which typically only 30 represent positive examples.


Similarly, since the labelers are guided to prefer more general versions of the CDC, as long as the original text spirit is kept, determining where the CDC should start could be quite a subtle decision.

the exact CDC boundaries often rely on the semantics of the text, and not just on its grammatical structure.


Note, that on average, there are around 30 labeled CDCs per Topic.

on average, the maximal precision at 50 should be around 0.6.


In addition, argumentative writing support systems will enable tailored feedback by incorporating argument recognition.

it could be expected that they provide appropriate guidance for improving argumentation quality as well as the student's writing skills.


It is based on the Argumentation Markup Language (AML) that models argument components in a XML-based tree structure.

it is possible to derive argumentative relations between components though they are not explicitly included.


They apply textual entailment for identifying support and attack relations between arguments and utilize the resulting structure for identifying accepted arguments.

they annotate a pair of arguments as either entailment or not.


For instance, in example (4) the premise in the second sentence is a reason or justification for the claim in the first sentence and the claim in (4) attacks the major claim of example (2).

an argumentative relation between two components indicates that the source component is a reason or a refutation for the target component.


According to these findings, we define a new top-down annotation process starting with the major claim and drill-down to the claims and premises.

the annotators are aware of the author's stance after identifying the major claim.


In addition, there are few differences regarding the boundaries of major claims (α U = 0.773).

annotators identify the sentence containing the major claim as well as the boundaries reliably.


We harvested 3arif from a variety of Twitter users including newspapers, TV stations, political campaigns, among others, as well as individuals.

3arif is diglossic for MSA, the formal Arabic variety, and EA, the native Arabic dialect of Egypt.


Annotators are instructed that using multiple negation markers results in an affirmative sense.

المستحيل من ليس lys mn AlmstHyl (it is not impossible) means that the proposition is actually possible according to the speaker.


In this version of 3arif, we work on the present and past tenses only.

task 4 is to decide for each valid epistemic trigger from task 1 whether it is present (PRS) or past (PSt).


Interest is given to writers' beliefs only.

a default value for the modality holder is the writer, and nested holders are not an-notated.


Define the global frequency of k-Freq(k)-as the number of times k is chosen by someone, divided by the total number of individual annotations.

if Freq i (k) is high, particularly if Freq i (k) > Freq(k), we may want to give a relatively low weight to any instance of annotator i choosing catgeory k. Every BCR defines a family of weights w ik , specifying for each annotator i ∈ N and each category k ∈ K how much weight to give to i's choice of k: In case of a tie, we assign category 'undecided'.


Table 1 defines the weights for four specific BCR's.

for example, if an annotator uses k in 50% of the cases, while the general population only uses k in 20% of all cases, then under Diff she has weight 0.7 whenever she chooses k. Note that Com and Inv do not take global frequencies into account, while Diff and Rat do.


Then the expected value of Freq i (k) is 1 |K| , i.e., it does not depend on acc i at all.

the two approaches track entirely different parameters, yet both achieve respectable results.


This approach is very sensitive to the results of the first iteration.


The annotation output is often in terms of syntactic heads.

we extend the boundaries of entity, time, and value mentions (e.g., people's titles) to include an entire phrase where possible.


On the other hand, if the set is large but contains many 'ambiguous' or 'weak' objects, a system suffers low precision.

one of the keys for success of relation mining is how to gather a large initial set, which are effective, i.e.


To maximize the effectiveness of the space, we performed preliminary experiments by changing parameters in the definition of context in the distributional hypothesis, such as how the context around entities is distinguished, whether the whole of a sentence or limited windows around entities are used as context, etc.

we chose the settings in which right, left, and intervening contexts are distinguished.


The measure assumes that a semantic space would be judged as appropriate if it assigned higher similarity scores to entity-pairs the relationships of which belong to the same category.

the measure evaluated the top 10 similar pairs to each entity-pair and calculated average precision defined as 10 t=1 Rel(t) • Pre(t)/10.


The previous work and Relation have no ways of changing the size of windows.

these results are independent of the window size.


However, in our experiment the context instances of a target word for WSI are derived from different articles, whose topic distributions are varied.

both lexical and contextual topics are modeled.


labels each usage of a target lemma with an induced sense.

any approach to identifying keywords -words that are substantially more frequent in one corpus than another -can potentially be applied to identify novel senses, by viewing "words" as (word,sense) tuples.


The underlying idea is that syntax may help to find important content.

instead of using hand-crafted rules, parsers, or language models, a simple and robust graph-based method can be used to generate reasonable summaries.


A common technique is called margin-rescaling and implies to scale the margin with the actual loss that is induced by decodingp instead of p i .

rescaling the margin by the loss implements the intuition that the confidence of rejecting a mistaken output is proportional to its error.


Note that cutting planes treat one input (G, p) at a time.

several models can be trained independently in parallel on disjoint subsets of the data.


These forums use a substantially less formal language than news articles, and at the same time address a much broader spectrum of topics than product reviews.

they present new challenges for automatic summarization.


In practice, however, not all generation methods are equally good: if a method does not generate patterns that adequately capture context, then employing the resulting patterns may yield poor-performing systems.

we employ only the system corresponding to the generation method that yields the best performance on the development data, which turns out to be the system corresponding to Method 3.


Implicit relations are very widespread in naturally-occurring data.

they make up between 39.5% and 54% of the annotated examples in the Penn Discourse TreeBank (PDTB) (Prasad et al., 2008), depending on the relation types used.


Most of these latter errors are mostly imputable to wrong classifications of implicit relations.

the current best accuracy performance on explicit PDTB relations are 94.15% on 4 relations , and 86.77% on 16 relations (Lin et al., 2010).


6 We have too few annotated examples to be able to construct a separate test set sufficiently large to make statistical significance test.

we decided to make a stratified nested cross-validation.


Traditional reinforcement learning requires thousands of dialogues, which are difficult to collect with real users.

a user simulator which simulates the behavior of real users is used for generating training dialogues.


In contrast, in persuasive dialogue, the system convinces the user to take some action achieving the system goal.

in this setting, reward corresponding to the achievement of both the user's and the system's goal is given to the system.


Neural fitted Q Iteration uses a multilayered perceptron as the Q-function approximator.

even if the Q-function is complex, Neural fitted Q Iteration can approximate the Q-function better than using a linear approximation function 1 .


As described in Section 1, we focus on developing cooperative persuasive dialogue systems.

the system must perform dialogue to achieve both the system and user goals.


The number of all possible GPF/framing pairs is 99, and some pairs have not appeared in the original corpus.

we reduce the number of actions by filtering.


However, in this work, we use Wizard of Oz in place of automatic speech recognition/Spoken language understanding (Section 6.2).


Compared to other works (Meguro et al., 2010;Misu et al., 2012), we are using a relatively small sized corpus for training the user simulator.

the user simulator cannot be trained to accurately imitate real user behavior.


In addition, in this research, corpus collection and evaluation are done in a role-playing situation.

we plan to evaluate the system policies in a more realistic situation.


It is defined purely on morphological grounds, without semantic validation of derivational families.

it contains a substantial number of words that are not semantically related.


The preceding analysis of DERIVBASE has established that the lexicon contains a substantial number (around one fourth) of lemma pairs that are not semantically related.

it is in need of semantic validation, i.e., a computational procedure that can filter out semantically unrelated words.


More specifically, the semantic similarity of related lemmas (R) is systematically underestimated, because the lemma pairs from our sample are often too infrequent to share any dimensions.

they receive a low or zero cosine even when they are semantically strongly related.


the target lemmas receive a low distributional similarity.


This ensures that less fluent (in this case, unseen) word combinations are substituted with more fluent (seen) ones.

even though an original AN important conversation in our dataset is correct, it is still "corrected" by this system to serious conversation.


Conversational style context and 140-character limitation in micro-blogs require users to express their thought or reply to others' messages within a short text.

without being ambiguous, some words are usually abbreviated with a special spelling.


More specifically, one of the focus of the project is to analyse the brand image of politicians on Twitter.

data about the two main candidates (F. Hollande and N. Sarkozy) in the last French presidential election in May 2012 have been crawled from Twitter, using Twitter API, from 6 months before to 6 months after the elections.


Unlike the character-level variation we described above, this type of variation occurs on a word level one.

there are both character-level and word-level non-standard tokens in Japanese informal written text.


The proposed lattice generation algorithm generates a lattice larger than that generated in traditional dictionary-based lattice generation.

we need to introduce an appropriate normalization cost into the objective function.


Obviously, the superimposition of tags in the WEB method may change the tag of a tweet word such that it results in an unlikely tag sequence, as we will discuss later.

we also implemented type-constrained decoding (Täckström et al., 2013), i.e., prune the lattice such that the tweet words observed on the website have one of the tags they were labeled with on the website (soft constraints), or, alternatively, were forced during decoding to have the most frequent tags they were labeled with (hard constraint decoding), thereby focusing on licensed sequences.


While the second baseline performs well on held-out data from its own sample (90.3% on GIMPEL-DEV), it performs poorly across our out-of-sample test and development sets.

it seems to overfit the sample of tweets described in Gimpel et al.


(2005), we are also interested in labeled attachment and the projection of POS annotation.

we copy labels through the alignment using the heuristics listed above.


However, one-to-many alignments do not always refer to proper multi-word expressions but often represent other grammatical or structural differences like the relation between the English preposition 'of' which is linked together with the determiner 'the' to the German determiner 'der' in sentences like 'Resumption OF THE session' translated to German 'Wiederaufnahme DER Sitzung'.

we decided to label these additional dependency with a new unique label dummy instead of selecting an existing one.


No large-scale hand aligned structures inside the parallel sentences are usually available to the SMT community, while the aligned structures are essential for training the translation model.

various unsupervised methods had been explored to automatically obtain aligned structures inside the parallel sentences.


There are diversified standards for grammar refinement.

the word classification or category refinement can be conducted in distinct manners, while each of them is reasonable in some sense.


However, in Chinese, it is often hard to tell the subordinating clause from the main clause in the compound statement.

in the prospective of linguistic computing, the confusion is that, CS and conjunctive adverbs both precedes the subordinating clauses or main clauses, while CC connects two phrases or precedes the main clause.


This result in a general "conjunction" category, within which we just enumerate all the possible uses of the conjunctions.

the structure of our human-defined taxonomy is fairly flat, as briefly shown in Figure 1 and Figure 2.


In fact, extra sub-symbols may need to be added to several nonterminals before they can cooperate to pass information along the parse tree.

we go in the opposite direction, and propose an extended version of split-merge learning to handle the multiple branches in the taxonomy.


Although the taxonomy of function words are learned in the grammar training process, the grammar is trained on the Treebank in supervised manner.

this work is not directly relevant with unsupervised grammar induction literature (Headden III et al., 2009;Berant et al., 2007;Mareček andŽabokrtskỳ, 2014).


On the contrary, the probability of the discontinuous orientation is higher if the previous phrase pairs contain verbs such as ⟨"gaishan", "improve"⟩.

the new model is capable of capturing the phenomenon that the orientation of a phrase pair depends on its surrounding contexts.


We observe that about 90% of reordering examples (i.e., the current and previous phrase pairs) are observed only once in the training data.

it is more difficult to train lexicalized reordering models accurately using maximum likelihood estimation.


(2011a) find that minimizing the norms of hidden layers leads to the reduction of reconstruction error in an undesirable way.


Therefore, many hypotheses are not likely to be recombined if the neural reordering model is directly integrated in decoding, making the decoder to only explore in a much smaller search space.

4 we use Moses to generate search graphs and then use hypergraph reranking (Huang and Chiang, 2007;Huang, 2008) to find most probable derivations using the neural reordering model.


In other words, training examples labeled with M, S, D are prone to be mixed with each other in the vector space.

it is difficult to find a hyperplane to separate M, S and D examples in the high-dimensional space.


As the left/right orientations only consider whether the current source phrase is on the left of the previous source phrase or not, unaligned source words will not change orientations.

for example, both figure 2 using left/right orientations in the neural reordering model not only has a higher classification accuracy (85%) over using the M/S/D orientations (69%), but also achieves higher BLEU scores on all NIST datasets systematically.


In language modeling, experimental results in (Mikolov et al., 2011;Arisoy et al., 2012;Sundermeyer et al., 2013) showed that recurrent neural networks (RNNs) outperform feed-forward neural networks in both perplexity and word error rate in speech recognition even though it is harder to train properly.

in this paper we take the advantages of RNN and tuple sequence model and propose recurrent neural network-based tuple sequence models (RNNTSMs) to improve phrase-based translation system.


The main reason may be lie in: the decomposition can provide wordto-word translation probabilities (such as "musicians:musiciens" in the example of Section 2) for those non-one-to-one alignments.

the translation system will have a translation option for an isolated occurrence of such words.


So did the Proto-Italic language to the romance languages such as French and Italian.

the values of the probabilities in German should be more similar to those in Dutch than to those in French or Italian.


First of all, unlike grammatical errors, explicit feedback such as correction by teachers is not normally given to language learners in the case of the use of the arbitrary linguistic items since any choice among the candidates allowed in a linguistic item is normally correct, as in the adverb positions in English: Already, I have done it., I have already done it., and I have done it already, although each of which might have a slightly difference in meaning.

language learners have little opportunity to adapt the values of the probabilities in their probabilistic module to those in the target language in the first place.


Tweet 6: ♥ @USER Below are two tweets from a 40 year old biological female who does not employ linguistic markers strongly associated with males or females.

only 46% of the crowd (n=200) was able to guess that she is female.


Twitter users vary in how much they emphasize their gender in their tweets.

the difficulty of inferring gender from tweets varies across persons, and treating gender as a binary variable ignores much of the interesting variation within and between persons.


Sampling We have shown that the difficulty of tasks such as gender and age prediction varies across persons.

creating datasets for such tasks requires maximum attention.


CFGR categories as variables As mentioned above, the best performance is achieved by combining CF GR ph and CF GR lex into the CF GR ph∪lex feature type using the weighted variationist feature representation var w .

we focused on that feature type and explored it more in depth.


The few relatively well-performing models we discussed in section 3.2 apparently are flooded by the noise introduced by the others.

for a set of rather low-performing models without any optimization, it seems preferable to provide the classifier with access to the individual features instead of to the noisy probability estimates.


Applying tuning without optimization (-opt, +all) outperforms the optimization result.

including the overall model [all] in the ensemble improves the meta-classifier.


Furthermore, as this metadata is widely available online (e.g., Amazon.com, IMDB.com) 2 , our approach is unsupervised in the sense that no manual annotation is needed for summarization purposes.

we hypothesize that summarizers guided by review helpfulness will outperform systems based on textual features/heuristics designed for traditional genres.


The goal of our human evaluation is to compare the effectiveness of 1) using a traditional content selection method (MEAD+LexRank), 2) using the traditional method enhanced by review-level helpfulness filtering (HelpfulFilter), and 3) using sentence helpfulness features estimated by sLDA plus review-level helpfulness filtering (HelpfulSum) for building an extractive multi-document summarization system for online reviews.

we use a within-subject design in our user study for each review domain, considering the summarizer as the main effect on human evaluation results.


For example, consider two aligned sentences from the aligned corpus described in Woodsend and Lapata (2011) An automatic comparison of the dependency parses for the two sentences reveals that there are two typed dependencies that occur only in the parse of the first sentence, and one that occurs only in the parse of the second.

to convert the first sentence into the second, two dependencies need to be deleted and one inserted.


In the case of referring expressions, pointing has been treated as a property, on a par with an object's colour or size.

van der Sluis and Krahmer (2007) propose an algorithm in a graph-based framework (Krahmer et al., 2003) which selects pointing gestures of varying degrees of precision based on their cost when compared to other linguistically realisable features.


Deixis: this has the value true if the utterance contains a demonstrative pronoun (such as that), or a reference to the landmark that identifies it with the previous landmark.

this feature is true if ID > 0 or D > 0 in Table 1; 2.


Typically, a given text will not contain a given acrostic but has to be reformulated using different wording or formatting to achieve the desired effect.

we consider the purposeful generation of acrostics a challenging benchmark problem for paraphrasing technology, which is subject to soft and hard constraints of common language usage.


the negative ∆ ARI and ∆ SMOG) for almost all acrostic types.

one may argue that the quality is not harmed too much; still some issues like wrong hyphenation are ignored by the metrics (cf.


This can be done by combining machine translation (MT) and information retrieval (IR): machine translation is used to transform the input source string to a coarse translation, which serves as a query to retrieve the most probable translation in the monolingual corpus.

it is possible for translation retrieval to have access to a huge volume of monolingual corpora that are readily available on the Web.


(2012) use 1-best translations, which are inevitably erroneous due to the ambiguity and structural divergence of natural languages, as queries to the IR module.

translation mistakes will be propagated to the retrieval process.


3 Query Lattice for Translation Retrieval Let f be a source-language string, E be a set of target-language strings, the problem is how to find the most probable translationê from E. Note that E is a monolingual corpus rather than a parallel corpus.

string matching on the source side (Sato and Nagao, 1990;Nirenburg et al., 1993;Baldwin and Tanaka, 2000;Baldwin, 2001) does not apply here.


To make things worse, there are usually very few variations in n-best translations because of spurious ambiguity -a situation where multiple derivations give similar or even identical translations.

we need to find a more elegant way to enable the retrieval module to explore exponentially many queries without sacrificing efficiency.


We can also find all the same three underlined words in the translation with the same order as they appear in the reference.


• FAUST 1 , which is out-of-domain and difficult to apply to our setting as the evaluations and postedits are user feedbacks, often in the form of phrases/fragments.

we instead attempt to predict automatic metric scores as there is a sufficient amount of parallel text for our language pair and domain.


Tree kernels are kernel functions that compute the similarity between two instances of data represented as trees based on the number of common fragments between them.

the need for explicitly encoding an instance in terms of manually-designed and extracted features is eliminated, while benefitting from a very high-dimensional feature space.


The difference between source and target in the models that use constituency trees is especially substantial and statistically significant.

it is apparent that the suspected lower quality of constituency parse trees of MT output is not the reason for the lower QE performance.


We found that tree kernels are a convenient and effective way of encoding syntactic knowledge but that our hand-crafted feature set also brings additional, useful information.

of comparing the role of source and target syntax, we also found that the constituent structure in the FTB could be amended to be more useful in QE for MT and parser accuracy prediction.


For top-100 this coverage rate increases only marginally (by only ≈0.5%).

considering only the top-40 entities in the candidate lists (which in general might contain hundreds or even thousands of entities), is not only an efficient but also an effective pruning strategy.


For example, in "He fell because the chair was broken", there is a dependency relation mark (broken, because), where mark indicates the presence of a finite clause subordinate to another clause (de Marneffe and Manning, 2008).

we encode the dependency feature for the token because as mark (broken).


And Freebase WEX 2 supplies a mapping between Freebase types to Wikipedia articles.

we can map Wikipedia articles to defined fine-grained types.


Note that this system still incurs cost for some rightbranching structures, such as in Figures 1(b-c), and some center-embedded structures.

for the arc-eager system, it is complicated to discuss the required order of memory cost.


We argue that the difference lies in the form of the recognized constituency tree: The former RIGHT-PREDs at step 4, which means that it recognizes a constituency of the form ((a b) c), while the latter recognizes (a (b c)) due to its RIGHT-COMP operation.

the spurious ambiguity of our system is caused by the ambiguity of converting a dependency tree to a constituency tree.


The relation between verb 打 (hit) and object 这个学生 (this student) is clear to indicate sentence S2 is an Attack event mention since the object is a person, while object 电话 (phone) in sentence S3 is not a person and it indicates this sentence is not an Attack event mention following the sense of verb 打 (call).

the object is an effective evidence to indicate event mentions and it is incorporated in our model to remove pseudo event mentions as follows.


Computing alignments between resources can sometimes be viewed as a task of disambiguation words or concepts in one resource by mapping them to the other resource (e.g., mapping Wiktionary entries onto WordNet senses).

the huge body of work on word sense disambiguation (WSD) is relevant, too.


• wmt13b: post-editing time, that is, the time that the post-editor has spent correcting the MT output.

the set of scores have different characteristics: in wmt12, the distribution is highly discrete due to the integer values assigned by the judges.


Yet this observation of "tightened" predicted scores cannot be fully understood without taking into account the risk of error in the prediction process, as it was visible on the scatter plots in figure 1.

it is also useful to look at the sorted scores, but with their corresponding predicted score (for the same sentence) plotted on the same x coordinate; this what is shown on figure 4, for the wmt13a dataset only (because the phenomenon is the most accentuated in this dataset, and scores conveniently belong to [0, 1]).


This is because, compared to the gold scores, the top or bottom predicted scores are closer to the centre of the range.

the sentences taken into account include some actual "tails sentences" (for which the absolute error is high), but they can also contain many sentences which actually belong to the area (for which the absolute error is low).


Unfortunately, because of the very difference in the use of absolute scores, they cannot be directly compared on this.

we focus here on empirical exploration of the nature of the problem in estimating quality in the case of supervised learning.


For example, suppose SDMM rules in Figure 4 and Figure 6 occurs 9 and 1 times respectively, we can compose them into the form as shown in Figure 7.

the composed SDMM rule will be represented by the original HPB rule <借 了 X, borrowed X> together with RDT and its frequency information shown in Table 1.


The SN is a high level ontology, and therefore to assume that all relationships between Classes are applicable to all instances would have produced many incorrect assertions, such as "All Drugs have the set of All Drugs as ingredients".

only instances of relationships that explicitly linked individual concept IDs (Concept Unique Identifiers, CUIs) were used.


There should be an equal number of positive and negative examples of a semantic relation.

random guessing or labelling with the most frequently seen label in the training data will yield 50% accuracy and precision.


These co-hyponym pairs were labelled as positive and the entailment pairs were labelled as negative.

this provides a balanced data set where half of the pairs are positive examples of cohyponyms and the other half, the negative examples, are entailment pairs (with direction unspecified) In both these sets, the average path distance between entailment pairs is 1.64, whereas path distance between co-hyponym pairs is 2.


However, events in GENIA can take only entity mentions as arguments.

instead of non-entity mentions, coreferent entity mentions that are the closest to triggers are annotated as arguments in GENIA.


Specifically, we assume that an instance is extracted by different patterns independently.

given all the pattern-instance relations (i.e., whether a specific pattern extracts a specific instance), the likelihood for an instance e being positive is computed as: where R + is all the patterns which extract e, and Ris all the patterns which do not extract e. I + is the set of all positive instances.


For instance, due to the lower overlap between categories Fruit and Capital, Apple is not a discriminant negative instance since it provides little information for the expansion boundary determination.

the instances near the threshold are used as the discriminant negative instances in the next iteration.


Several papers have shown that the experimental performance may vary with different seed choices (Kozareva and Hovy, 2010;McIntosh and Curran, 2009;Vvas et al., 2009).

we input the ESE system with five different positive seed settings for each category.


However, only 14.94% of the instances in the PTB consists of words with GP greater than 1.5 and 45.71% consists of words with GP exactly 1.

the overall accuracy numbers do not adequately reflect the improvement on highly ambiguous words.


Prior to deriving the word language models, a sentence consisting of a single UNKNOWN token is appended to the training corpus.

word n-grams that include an UNKNOWN token are assigned very low probability, encouraging the solver to favor decipherments containing fewer OOV words.


In the initial stage, n-grams are ranked according to the score computed using the method described in Section 3.

the potential replacements for a given ciphertext n-gram are the highest scoring p-equivalent n-grams from the training corpus regardless of the form of the decipherment implied by the current key.


It is almost certainly too much to expect a single fixed transformation to be able to capture the meaning combination effects of all natural language operators.

mVRNN assigns a matrix to every word and modifies the meanings of other words instead of only considering word embeddings in the recursive procedure.


(2016), we introduce a task-specific vector e t (task embedding) which is first combined with s t , b t , a t to compute p t , and then further concatenated with p t to compute the probability distribution of transition actions.

eqn 2, 3 become: The joint cross-entropy is used as the objective function.


However, the vocabularies (word, characters) are language-specific.

it makes sense to share the lookup tables (embeddings) of POS tags (E pos ), relations (E rel ) and actions (E act ), but separate the character embeddings (E char ) as well as the Char-BiLSTMs (BiLSTM(chars)).


POS tags of the heterogeneous treebanks into universal POS tags (Petrov et al., 2012).

e char and BiLSTM(chars), e pos are shared across tasks, but e rel , e act , LSTM(A) are task-specific.


The two cases require different algorithms: while causal constructions containing causal verbs are quite straightforward to identify, causal signals are very ambiguous and can appear in different syntactic constructions.

6 we tackle the first through a rule-based approach, while the second is best covered via supervision, taking advantage of the freely available Causal-TimeBank.


), appear in advance of flu epidemic occurrence.

past word occurrence can contribute to estimation of the number of current patients.


To analyze a word, we applied a Japanese morphological parser (JUMAN 3 ) and obtained the stem forms.


However, in the case of vocabulary size |V |, which is much larger order than sample size T , it has been observed empirically that the prediction performance of l 1 -penalized regression, the Lasso is dominated by the l 2 -penalized one.

we employ the Elastic Net (Zou and Hastie, 2005), which combines the l 1 -penalty and l 2 -penalty 2 ), where α is called l 1 ratio.


Lasso and Enet have no features for predicting future values.

we use Lasso+ and Enet+ for forecasting.


In addition, there were many highly correlated features by shifting around 10 days into the future.

our model demonstrated equivalent performance up to 10 days into the future.


In spite of the word "shot" is the largest weighted feature in the case of τ min = 21 and Train in Season 3, these word correlations were 0.310 in Season 1 and 0.03 in Season 2.

it caused a considerable decrease of the forecasting accuracy.


We thus claim that understanding the properties of the task and mapping them to the desired properties of the evaluation measure is crucial when selecting a measure for an intrinsic evaluation.

we propose in section 4 a new framework on the intrinsic evaluation of STS systems by taking the requirements of the target task into account.


While it has been translated using the expand approach to many other languages, this is an expensive manual process.

it would be beneficial to have a high-quality automatic translation approach that would support NLP techniques, which rely on WordNet in new languages.


Since most of the current non-English wordnets use the Princeton WordNet as a pivot resource, concepts, which are not in this English lexical resource cannot not be realized or aligned to it.

the authors support the idea of a central platform of concepts, where new concepts may be added even if they are not represented (yet) in the Princeton WordNet or even lexicalized in English (e.g., many languages have distinct gendered role words, such as 'male teacher' and 'female teacher', but these meanings are not distinguished in English).


Even with a very large parallel corpus, as we increase the number of languages, in which we disambiguate the sense, we find that for many senses we cannot find a context that is disambiguated in all languages.

we evaluate the impact of changing the number of languages used to disambiguate an English sentence.


require parallel data between the source and target views (where a view could be a language or some other modality like image).

given n views, we require n C 2 parallel datasets to build systems to convert from any source view to any target view.


This decrease results directly from the word order difference between French and English, as English adjectives are almost always preposed 6 while their position in French is less deterministic: in the French UD, 28% of the adjectives occur before their head noun and 72% after it.

the UAS NOUN ADJ score on French actually decomposes as 96.8% for UAS NOUN preposed ADJ and 34.5% for UAS NOUN postposed ADJ .


Søgaard (2011) follows similar intuitions but binarizes the weights to apply instance selection.

the delexicalized model is trained only on the source examples that are the most relevant for the target at hand, using PoSLM perplexity as a relevance metric.


Online reviews on products and services are extensively used by consumers and businesses for conducting decisive purchase, making product design and altering marketing strategies.


We experimented with both CNN and RNN for both sentence and document modeling, finding that the best development accuracies are obtained when CNN is used for sentence modeling and RNN is used for document modeling.


Taking Figure 2(a) for example, true Customer reviews in the test set are shown in black, where false reviews by Employee are shown in red.

black dots on the top of the figure and red dots on the bottom show cases which the discrete model predicted correctly, while black dots on the right and red dots on the left show cases which the neural model predicted correctly.


The customer stops at the i-th table with probability η i , and passes it with probability 1 − η i .

the probability that the customer stops at the i-th table is given by This probability decreases exponentially.


This is because such OOV words including unknown hesitations are actually mis-recognized and assigned similar-sounding words in the ASR vocabulary.


Our model will provide criteria for which words or symbols should be skipped.

the model integration of ours and the OOV-free model (Mochihashi et al., 2009) is required to process actual conversational utterances.


The actual fillers/hesitations and mis-recognized words move from head to tail in the context in predicting words sequentially.

if the unknown segment is away from the context root, we can use the neighbor context without risk.


Instead, training data can be automatically generated from semi-structural data such as Wikipedia pages (Ling and Weld, 2012).

a single supervised classifier (Ling and Weld, 2012;Yogatama et al., 2015) or a series of classifiers (Yosef et al., 2012) are trained on this autoannotated training set.


A bidirectional LSTM creates a representation g x of an input sequence x = (x 1 , x 2 , ..., x T ) by computing a forward sequence − → g and a reverse sequence ← − g over the input sequence, concatenating the results of the two sequences, and applying a rectified linear unit (ReLU) activation where f (x, y) is the output of an LSTM unit with inputs x and y.

we create a representation of the stem by taking the characters of (stem i,1 , stem i,2 , ..., stem i,K ) as the input sequence for a "stem" LSTM and a representation of the tags by taking (tag i,1 , tag i,2 , ..., tag i,L ) as the input sequence for a separate "tag" LSTM.


When decoding, we use a greedy approach to select the parse to add to the previous parse LSTM.

our objective is to predict the output sequencê Locally normalized models suffer from the label bias problem (Andor et al., 2016), meaning that they have little to no ability to revise previous decisions.


For the requirement of the second term a ∞ ≤ ϵ q , recalling (19), it goes to γ ≤ cϵ−2τ q qκ 2 .

introducing a real value β ≥ 1, we can set γ as γ = cϵ − 2τ q βqκ 2 (24) Note that, to make this formula meaningful, it is required that cϵ − 2τ q ≥ 0.


Thus, introducing a real value β ≥ 1, we can set γ as γ = cϵ − 2τ q βqκ 2 (24) Note that, to make this formula meaningful, it is required that cϵ − 2τ q ≥ 0.

it is required that which is solved by the condition of (11).


On the other hand, we analyze the requirement of the first term that (1 − cγ) t (a 0 − a ∞ ) ≤ ϵ q .

since a 0 − a ∞ ≤ a 0 , it holds by requiring (1 − cγ) t a 0 ≤ ϵ q , which goes to since log (1 − cγ) ≤ −cγ given 7, and that log ϵ qa 0 is a negative term, we have setting t .


The preceding sentence selection focuses solely on informativeness and totally neglects coherence.

it prevents the improvements expected from permutation.


Previous work on deep learning with graph structures suggests convolutional neural networks can exploit similarities in structure (Kalchbrenner et al., 2014;Niepert et al., 2016).

we developed analogous techniques to encode supertags based on their underlying tree structure.


Table 2 shows the results on the test sets.

when evaluating on the DSTC2 test set, a window of 4 (w4), performs slightly better than other window sizes and better than the simple CNN model.


The discussion in Section 2 has shown, that the Sanskrit grammatical tradition does not provide a solid basis for developing a practical annotation guideline for SBs.

ten sequences of at least two metrical lines that contain complex syntactic phenomena were annotated independently by three external annotators and the author of the paper.


An unsophisticated text such as the MBH, for example, uses 35 synonyms to denote the warrior Arjuna, or 14 for the concept "mountain".

one faces considerable data sparsity, when lexical information is used in 1-hot encoding.


While the character component learns general regularities that are shared between all the words, individual word embeddings provide a way for the model to store word-specific information and any exceptions.

while we want the character-based model to shift towards predicting high-quality word embeddings, it is not desireable to optimise the word embeddings towards the character-level representations.


While optimising the hyperparameters for each dataset separately would likely improve individual performance, we conduct more controlled experiments on a task-independent model.

we use the same hyperparameters from Section 6 on all datasets, and the development set is only used for the stopping condition.


In such situations, it seems easier to first assign a highly generalizing model that can be learned from relatively little training data instead of forcing the model to make an uninformed fine-grained decision.

in this section we explore how well coarse-grained tagging actually works.


If we wish to optimize the detection accuracy for VBN in our approach, we can ignore the performance on the other tags.

we transform the problem into a one vs. all classification, i.e.


Classic paragraph embedding methods infer the representation of a given paragraph by considering all of the words occurring in the paragraph.

those stop or function words that occur frequently in the paragraph may mislead the embedding learning process to produce a misty paragraph representation.


In other words, the frequent words or modifiers may overshadow the indicative words, thereby drifting the main theme of the semantic content in the paragraph.

the learned representation for the paragraph might be undesired.


On a separate front, with the popularity of the Internet and the increasing development of the digital storage capacity, unprecedented volumes of multimedia information, such as broadcast news, lecture recordings, voice mails and video streams, among others, have been quickly disseminated around the world and shared among people.

spoken content processing has become an important and urgent demand (Lee and Chen, 2005;Ostendorf, 2008;Liu and Hakkani-Tur, 2011).


In other words, the frequent words or modifiers may overshadow the indicative words, thereby making the learned representation deviate from the main theme of the semantic content expressed in the paragraph.

the associated capacity will be limited.


That is to say, the proposed paragraph embedding method EV can indeed distill the most important aspects of a given paragraph and meanwhile suppress the impact of the general background information for producing a more discriminative paragraph representation.

the relevance degree between any pair of sentence and document representations can be estimated more accurately.


However, AuToBI has several limitations.

it outputs word-by-word annotation, which is a handicap for obtaining a higher-level representation.


We establish different levels of abstraction, which do not strictly correspond to theoretical representations as such, but follow the idea that prosodic units to be tagged must follow a certain parametric logic in terms of positive or negative deviations at each level in their associated segments.

we consider as Level 1 (L1) the whole utterance or speech sample; as Level 2 (L2) each voiced segment within the utterance; Level 3 (L3) PPhs once they are segmented.


Note that promoting the most probably correct analysis, whether containing MWEs or not, is the goal of probabilistic parsers in general.

instead of designing a custom parsing architecture for promoting MWEs, it would be more adequate to simply train a general-purpose parser on a treebank containing MWE annotations.


those containing grammar-compliant idiomatic interpretations.

we do not quit when the first target item has been reached, but only when we are sure that no more optimal derivations can be found.


Note that in case of parsing with a flattened TAG, only an ET t, not its individual flat rules, is assigned a weight.

t's weight contributes to β(I) only when t has been fully parsed, and it contributes to h(I) otherwise.


The experiment was carried out on the same dataset from which the grammar was extracted.

for each sentence, the baseline hypergraph contained both its gold (i.e., conforming to Składnica) parse (derived tree) and its gold MWE identification (derivation tree).


The PM hypergraphs, in turn, contained the correct parses for virtually 100% of the sentences, 8 and correct MWE identification for around 95% of them (due to the idiomaticity rate equal to 0.95).

the parsing efficiency gain due to the PM strategy occurred with no loss of accuracy.


For the words absent from in the initial parallel corpus, their vector representation is a vector of zero values.

during testing, the RNN model will use only the context information to tag the OOV words found in the test corpus.


A major problem of this framework is that it suffers from noises produced by word alignment errors.

some de-noising methods have been proposed (Kim et al., 2010;Wang and Manning, 2014).


• To leverage character level information, for each word x L i , there is a character sequence where p is the number of characters in word X L i , and V Lc is the number of different characters in language L. Then, we project these input signals from high dimensional space of token id into dense vector spaces using look-up tables.

we have tag are look-up tables for English/IL word, English/IL character, and English tag respectively.


Similarly, for IL, we have To utilize information extracted from English side, we use h E f [n E − 1] and h E b [0] from the last steps of bi-directional LSTM to initialize the starting states of LSTM hidden layers in IL side.

during training, errors can be back propagated to English side neural networks through these two vectors.


This shows that annotation is quite consistent within each language.

despite the cross-lingual annotation inconsistency, our corpora appear to be internally consistent enough to train a model over, based on the observed MWEs in a language.


Online reviews for sentence based sentiment analysis in Hindi (Review S H ) 3 : There is no available benchmark dataset which deals with sentence-level sentiment analysis for online product reviews in Hindi.

we extract user reviews from Review A H dataset and annotate these using four classes as mentioned above.


Recently, there are more and more concern about how to incorporate more latent sematic features into the NER system (Konkol et al., 2015).

word cluster IDs function as a non local feature to improve the performance of NER system (Turian et al., 2010;Zirikly and Diab, 2015).


What's more, the NNBS suffixes in Mongolian can be located unambiguously, while other suffixes segmented may lead to some letters insertion, lost and substitution.

we proposed three methods to process NNBS suffixes.


UMLS is a very rich source of information in medical and biological domain.

most existing biomedical summarizers utilize UMLS as a large lexical and semantic medical ontology.


These semantic types are defined as quantitative concept, qualitative concept, temporal concept, functional concept, idea or concept, intellectual product, mental process, spatial concept, and language (Plaza et al., 2011).

only concepts of the rest of semantic types are considered for constructing the semantic signature.


We extracted features from training data as explained in Section 5.

26 million words were extracted for titles and 47 million words were extracted for descriptions.


Words appearing more than 50 times in the training data were selected as features for training first-step DBN and DAE networks.

the number of word features (dictionary size) was 968,471 for titles and 1,461,625 for descriptions.


Likewise, the content relevant to the query but not representative of documents will be excluded either.

in an isolated model, weights for neither query-dependent nor query-independent features could be learned well from reference summaries.


Apparently, even if a sentence is exactly the same as the query, it is still totally useless in the summary because it is unable to answer the query need.

the surface features are inadequate to measure the query relevance, which further augments the error of the whole summarization system.


Without these features, the performance of SVR only drops 1%.

sVR fails to learn query relevance well.


Obviously, it is very time consuming to manually produce high quality summaries for many popular topics.

automatic summarization for related news articles is essential to alleviate the manual work.


This is due to the long sentences and the summary length constraint.

next we investigate abstractive summarization by applying sentence compression when extracting sentences from news to improve summarization performance.


The local top node for the node set is through p (shown in the yellow box); it is connected to the noun predicate method n of with an outgoing NEQ link (shown in the pink box), and it is the only link connected with the set.

we replace the local top with the predicate be v there , and pass the resulting DMRS subgraph to the generator, resulting in the summary sentence There is the method of making fire.


Secondly, the data contains idiomatic, informative and loose translations, in which case we want the outputs of both systems to differ.

we also measure how closely the outputs of C&C/Boxer and our system resemble a gold standard of 150 sentence/DRS pairs from the testing portion, for their respective input languages.


Instead of training ngram probabilities on sentences (i.e., sequences of words), we can train them on words, wherein each word is a sequence of morphemes and morpheme boundaries.

to continue with the English example lighting, the bigram-MLE of the candidate segmentation *light=ing would be calculated as follows: Since the bigram 'X ing' is far more frequent than '# ing', P (#lightXing#) will receive a higher MLE than P (#light#ing#).


To utilize other types of graph context, one could first define c(s i ) and the algorithm used to extract the context from the given knowledge graph G. After that, the remaining steps for knowledge representation learning would be exactly the same with other types of graph context.

our framework is general and flexible to extend different types of graph context easily.


However, existing deterministic model is vulnerable to noise in the knowledge base.

we instead propose to leverage probabilistic approach to build a prototype that can overcome noise.


This paper studies the serendipitous property mining problem of finding relevant yet unexpected properties for a given entity based on a knowledge base.

our work is closely related to knowledge acquisition.


If we expect most mammals have a heart with the size in a specific range, the extra large heart of a giraffe can be unexpected.

defining the prototype of a category-which represents the human expectation for entities in the category-is the key step to find serendipitous properties.


Then, we can consider some property of an entity is unexpected if the property is particularly more frequently mentioned with the entity than with other entities in the category.

using the average of typicalities allows us to compute the representativeness of the property for the category (e.g., MAMMAL).


For example, 'lifespan' is a common property for the most of entities in MAMMAL.

high P (lifespan|giraffe) does not mean giraffe has an unexpected property.


For example, although the most entities in MAMMAL have a property 'neck,' it is not frequently mentioned, and hence we expect to have low P (neck|e) value.

the distribution of X neck,mammal is skewed to low values.


Upon this value, we also consider that a serendipitous property of a well-known or more typical entity can more easily draw user attention.

we quantify the degree of being serendipitous for property a of entity e that belongs to category c as follows.


U N EXP @N = RS@N − BM @N These unexpected recommendations may or may not be relevant to the query, but we want unexpected but still relevant ones.

we measure the serendipity based on the relevance of each item in U N EXP .


We may anticipate that the methods pursuing serendipity may have lower relevance, since such methods would avoid highly relevant results, which are often expected.

dM shows lower relevance than PM because it gives lower score to those close to the average, but it may instead acquire and leverage noisy properties as unexpected.


For example, DM may consider 'fourth piece' of Intel, which is extracted due to ambiguity and hence rare in the category, as unexpected.

it leverages such a property and in turn mines irrelevant trivia quizzes.


We build a reference ranking according to the several evaluation dimensions since "pleasant" in serendipity (pleasant surprise) can be interpreted in different ways.

we use several criteria such as 'more relevant questions rank higher' based on (Bordino et al., 2013) and evaluate individual dimensions.


The first and last segments on a line may be exceptional as they may be affected by large eye movements going from the end of the line to the beginning of the following line, or backtracking to reread content at the end of the previous line.

factors were included in the analyses encoding whether the segment is the first (is first), second to last (is second last) or last (is last) on a line.


the intercept) encodes the False value for binary factors.

a factor name followed by '1' (e.g., is first1) indicates what happens to the model prediction when the factor changes from FALSE to TRUE.


is first=FALSE, is second last=FALSE; is last=FALSE).


To compare the effects of paradigmatic representation (a * b) with the syntagmatic one (aX + Xb) we train and test both models using the identical 10-fold cross validation split.

every model in this paper is exposed to the identical sequence of training and testing patterns.


(2010) showed that flexible frames have richer distributional information than other frame types both in terms of classification accuracy and coverage.

we only report results of the models based on substitute words (a * b) and flexible frames (aX + Xb).


After 16 substitutes, increasing the number of substitutes does not significantly change the classification accuracy.

the model is fairly robust to the number of substitutes as long as the model can observe at least 16 substitutes per target word.


The perplexity of the language model is a measurement of the variation of words that can be observed in a given n-gram context window and is determined by n-gram order of the language model.

as the n-gram order increases the model assigns more relevant substitutes to the context 6 .


The [i, j] indicators present in the n-gram features of Table 1 refer to the number of tokens to the left (i) and right (j) of the words that was considered.

[0, 0] refer to single-word frequencies.


In the literature, there is a lack of operational definition of empathy.

we adopted the modal model of emotion by Gross (1998) in order to define empathy and design annotation guidelines for the annotators.


As mentioned earlier, empathy, Emp, has been annotated in the agent channel only.

the preceding context of agent's emotional segment is defined as P re.Emp.


The results show that performance differences are very low, only 1.2% drop with automated transcriptions (Alam et al., 2016).

we found that the use of automatic transcriptions are reasonable for the experiment given that manual transcriptions are not available in call cases.


The results indicates that compared to the preceding context of empathy (Pre.Emp) and no emotion (NoEmo) segments, participationEquality, M edianT urnC and M edianP auseC has a negative effect on empathy (Emp) segment, where as %Overlap and length of non-competitive overlap (M edianN cm) has a significant positive effect.

indicating the importance of non-competitive overlap in the empathic segment (Emp).


Yet, there is a difference in terms of the feature counts for the two models, namely, 16,841 vs. 18,174 respectively.

the version incorporating complex features is more efficient, providing the same quantitative advantage with a more compact model.


However, the difference is relatively small in comparison to the difference observed for 19cLM on 19th and 20th c. abstracts.

20cLM is better in predicting lexical choices in both 19th c. and 20th c. abstracts compared to 19cLM.


For example, the word bank cannot have high cosine similarity with the word river and money at the same time since these two words are so dissimilar and the single vector representation of word bank cannot express two different meanings.

several multi-prototype models have been proposed to alleviate the problem caused by the polysemy and homonymy.


In other words, the different contexts of a word exert different influences, and the given context is a good indication of the direction of the word meaning.

we propose a method to model the intrinsic quality of words and obtain the influences affected by contexts.


This move, and the feature types collected, reflect the assumption that speakers tend to indicate SU boundaries in consistent ways -by pausing before starting a new SU, by producing lengthened sounds in advance of an SU end, or by tell-tale discontinuities in pitch and volume levels either side of a <break>.

our feature choices are largely conventional, following the lead of Huang et al.


Several sentences are expressed similarly to show that the content in the sentences are equal in importance.

properly using parallelism may improve the quality of texts.


For all alignment measures, the basis is the alignment between single words.

we start by studying word alignment strategies and look forward to incorporate information from multiple aspects.


Longest Common Substrings (LCStr) Parallel sentences have a high chance to have common substrings.

we compute the longest common substrings of two sentences.


Different from LCSeq, the common substrings are required to occupy consecutive positions.

high LCStr indicates a better local alignment.


Gap penalty values are used to penalize the score when a word in one sequence is aligned to an indel in the other.

nW algorithm would penalize the long distance matches.


The parallelism can add the clarity in organization.

there are more cross-para chunks in argumentative essays.


We guess the reason is that since parallel sentences are used to impress the readers, the beginning and the ending parts are easier to draw readers' attentions.

students tend to put impressive sentences at these important positions.


Moreover, these formatting problems may cause an erroneous coreference chain where the effects of an early bad resolution are amplified while going further in the text.

to improve anaphora resolution (and then substitution) in the original text, we segment the original text into sections.


For example, in a biography there could be more people indicated by a common surname.

an arbitrary substitution of all proper name anaphors could be wrong, because the anaphora resolution software may fail to identify the correct subject: in our example, if the head of the coreference chain is "Hamilton", we risk to replace "Margaret Heafield Hamilton" with her husband, whose surname is Hamilton too.


It shares the formal description DET N GEN N INDEF with QUANTIFYING GENITIVE:TIME but is semantically restricted to scalable measures like height, depth, age, size and other things that can be quantified.

the query captures true instances of the scale construction like tusen meters höjd 'a thousand meter's height' and elva månaders hyra 'eleven month's rent', but also instances of the aforementioned time construction tre timmars seglats 'three hour's sailing trip'.


Furthermore, most earlier works in Japanese loanword phonology focus only on particulars or a handful of specific arguments revolving around a limited number of constraints, or assume that a set of rules or constraints discussed have covered all the phenomena; few works have actually given a complete overview of the process.

a need for a fuller description of the loanword system has motivated this study.


We observed that the adaptation of /@/ heavily depends on the original orthographic representation.

we coded each input /@/ with the written orthographic symbol it originated from.


Multiword Expressions (MWEs) are recurrent combinations of two or more words expressing a single unit of meaning, this meaning not always derivable directly from the meanings of the component words (Sag et al., 2002).

natural Language Processing (nLP) tasks that need to be sensitive to lexical meaning should treat MWEs as single units.


Healthcare providers conduct clinical trials to discover ADRs before selling the products but normally are limited in numbers.

post-market drug safety monitoring is required to help discover ADRs after the drugs are sold on the market.


Prepositions are a closed set of function words.

it is feasible to substitute each of all prepositions for the location of a preposition.


The major disadvantage of n-gram is that the number of its parameters growths exponentially as the order of n-gram increases.

the order of n-gram usually ranges between bigram and 5-gram.


As shown in Table 4, 18 instances of 在 (zài, "on/in/at") and 10 instances of 給 (gěi, "for/to") are misclassified to 對.

the recall of 給 (gěi, "for/to/with") and the precision of 對 are poor.


A typical application for unsupervised morphological segmentation involves situations in which we are confronted with a low-resource language for which no prior NLP work exists, and for which we cannot annotate even a small corpus (either for lack of time, or because no annotators are available).

in this paper, we are interested in remaining language-independent and entirely unsupervised.


We want to remain entirely unsupervised.

we perform a crossvalidation on the six languages, using the five development languages of one fold to determine which grammar to apply to the held-out unseen language.


Typically, only a very small number of lexemes are used to illustrate the morphology, or the affixes are simply listed without stems.

the data is not a representative (type or token) sample of actual words in the language.


Since the fork and join outcomes only allow a single derivation fragment to be initiated or integrated, each case of the active category model only nondeterministically modifies at most one a d t variable from the previous time step: 3 Decisions about the awaited categories b 1..D t also depend on the outcome of the fork and join variables.

again, since the fork and join outcomes only allow a single derivation fragment to be initiated or integrated, each case of the awaited category model only nondeterministically modifies at most one b d t variable from the previous time step: the parser has a fixed number of probabilistic decisions to make as it encounters each word, regardless of the depth of the stack.


Preliminary work found that mixing with token-level Gibbs sampling is even slower in this model due to the tight constraints imposed by the switching variables-it is technically ergodic but exploring the state space requires many low probability moves.

the experiments described in this paper use sentence-level sampling instead of token-level sampling, first computing forward probabilities for the sequence and then doing sampling in a backwards pass; resampling the parameters for the probability distributions only requires computing the counts from the sampled sequence and combining with the hyperparameters.


Some error types allow for straightforward generalisation from seen examples (e.g., errors in function words or mechanical errors), but errors in content words appear to be less systematic.

it is crucial to have sufficient, thoroughly annotated learner data.


Studies in readability from this period stressed the importance of higher textual dimensions, focusing on inference load (Kemper, 1983), conceptual density (Kintsch and Vipond, 1979), or organisational aspects (Meyer, 1982).

the classic lexico-syntactic features were disregarded for years.


The reason for this analysis had been put forward by Boyer (1992) who said "it is conceivable that there are relations between the surface features of the text measured by [classic] readability formula and text characteristics of higher level".

semi-partial correlation will help determine whether our variables contribute to text readability prediction with additional information besides sentence length and word frequency.


For English, Swedish, and French, third person singular pronouns have different surface forms based on gender.

in order to increase precision, we limit the detection to only include third person pronouns.


However, one drawback lies in the inclusion of entities ungoverned by any predicate.

the alignment of partial semantic content, as described in Sect.


It has to be noted that the Indo-European dating studies (Bouckaert et al., 2012;Chang et al., 2015;Rama, 2016) employ human expert cognacy judgments for inferring phylogeny and internal dates of a well-studied language family.

there is a need for developing automated cognate identification methods that can be applied to those families of the world that are not as well-studied as Indo-European language family.


(2012) are also used by human experts to derive the phylogeny information given in Glottolog.

we include the language information that is available with the word lists and hypothesize that a fully connected neural network layer can learn the weights of the language features jointly with the phonological representations generated by siamese CNNs through back-propagation.


Not only the information overload itself, but also the heterogeneity of the data poses an obstacle to the aggregation and assessment of information, as text structures, styles, and forms of information presentation vary across different genres and domains of text.

automatic summarization of multiple heterogeneous sources is a key research demand.


(2013) take the top ten results from a regular web search engine as the source documents for their summaries, but do not specify the types of documents used.

it is unclear whether their data is heterogeneous or not.


Step 2: Selection of best nuggets Redundancy is a more severe issue in MDS (Goldstein et al., 2000b;Goldstein et al., 2000a) compared to single-document summarization, as recurring facts are more likely to occur in multiple different documents about the same topic than within a single one.

we ask the annotators to group the selected nuggets from step 1 if they "convey the same meaning using different wording" (Bhagat and Hovy, 2013, p. 463).


But as we know, the words used in poems, especially poems written in ancient time, are different from modern languages.

the existing methods may fail to generate meaningful poems if a user wants to write a poem for a modern term (e.g., Barack Obama).


2016did not consider the generation of the first line.

the first line is provided by users and must be a well-written sentence of the poem.


Poeticness Fluency Coherence Meaning Average 5-char 7-char 5-char 7-char 5-char 7-char 5-char 7-char 5-char 7-char SMT  as BLEU and METEOR, have little correlation with human evaluation.

we carry out a human study to evaluate the poem generation models.


This different mechanism and the imperfections in the dependency trees make splitting on conjunctions a complex task and can lead to fragments that are not consistent.

we generate two datasets of different granularities from the original corpus: the first one does not involve any action on conjunctions while the second one contains fragments extracted from conjunctions.


A cluster is considered as sufficiently important if a fixed threshold µ is exceeded (line 12).

one sentence from the cluster is published (line 14).


, s K } we define the sim score of a similarity measure σ as the average similarity of all similar sentences and the dissim score of a similarity measure σ as the average similarity of all dissimilar sentence An appropriate similarity measure can perform both, assigning high scores to similar sentences and low scores to dissimilar sentences.

we measure both by computing diff(σ) = sim(σ) − dissim(σ).


The abstracts, however, are missing in 50,000 articles available within the open access subset of PMC.

for this large set of articles, the only way for the users to judge the relevancy of an article is either through the article title, which is not always reliable, or through the full-text of the paper, which can be time-consuming.


Thus it is well-suited to learning NLG systems where measures such as BLEU (Papineni et al., 2002) or ROUGE (Lin, 2004) that do not decompose over single words are commonly used for evaluation.

we are able to learn the interactions of the word currently being predicted with those to be predicted later in the sentence.


In LOLS the loss function is only used to compare complete action sequences against references (line 11).

it does not need to decompose over individual actions and allows us to use measures like BLEU (Papineni et al., 2002) or ROUGE (Lin, 2004) as loss functions.


Exam questions are an indispensable tool for teachers to assess their students' understanding of material.

automatic question generation from text is a key natural language processing technology to aid teachers in examining learners' reading comprehension.


Our question generation system is aimed at enhancing the reading comprehension ability of language learners, more specifically, students who learn English as a second language (ESL).

our ultimate goal is to generate multiple-choice questions from plain texts in an arbitrary domain.


However, the state-of-the-art in extracting semantic representations of event and entity relations from text does not perform well enough to support our question generation approach.

the evaluation of question generation relying on automated semantic relation extraction is not practical at this time.


In this sense, it follows in the footsteps of Generalized Phrase Structure Grammar (GPSG) (Gazdar et al., 1985), Lexical Functional Grammar (Bresnan et al., 2016), Head-Driven Phrase-Structure Grammar (HPSG) (Pollard and Sag, 1994) and Combinatory Categorial Grammar (CCG) (Steedman, 2000).

yet, different from these other approaches, it adheres to the principles of Construction Grammar (CxG) (Goldberg, 2005;Östman and Fried, 2005;Hoffmann and Trousdale, 2013).constructions are treated as first-class citizens in the grammatical organisation of a language.


This makes the precision 2/4 and the recall 2/5, leading to an F-score of 0.44, which is the maximum amongst all the possible bindings.

0.44 is also the Smatch score (S-score) for the two meanings.


Hence, the coverage of different contexts is an important consideration in the evaluation of such a grammar.

we built our own test suite by collecting 67 sentences from linguistic research papers dedicated to this phenomenon (Madeira, 1992;Luís et al., 2004;Luís and Otoguro, 2011), and annotated them manually.


(2015a) investigated the quality and quantity of a monolingual parallel corpus using the framework of statistical machine translation and showed that sentence pairs with a moderate level of similarity are effective for training text simplification models.

we use the sentence similarity method to accurately measure the moderate level of similarity.


Moreover, it is often the case that many word similarities φ(x i , y j ) are noise and are near to zero.

we utilize only accurate alignments by computing the sentence similarity STS asym (x, y) from the most similar word y j for each word x i rather than averaging the word similarities between all pairs.


Here STS asym (x, y) is an inherently asymmetric score.

we obtain the symmetric sentence similarity STS max (x, y) by averaging the two similarities STS asym (x, y) and STS asym (y, x) as follows: Average alignment and maximum alignment can be considered as sentence similarity measures based on many-to-many word alignments and many-to-one word alignments, respectively.


We aligned only those word pairs with a word similarity equal to or greater than 0.49, and aligned only those sentence pairs with a sentence similarity equal to or greater than 0.53.

we obtained 492,993 sentence pairs from 126,725 article pairs.


In a Wikipedia article, sentences are arranged in a characteristic order, e.g., a definition sentence appears in the first sentence.

they may obtain similar sentence pairs effectively.


Word embeddings capture the context of the words.

similar word vectors may correspond to synonyms or morphological variants (see section 2.3).


NER methods (typically trained on longer texts, such as news articles), have been shown to perform poorly on shorter and noisier social media content (Ritter et al., 2011).

recent Twitter NER work (Ritter et al., 2011;Liu et al., 2011;Derczynski et al., 2015) has focused on improving the state-ofthe-art, through new methods.


In crowdsourcing, successful tasks tend to present even fewer choices -in most cases between two and five categories (Sabou et al., 2014).

we chose to focus on the three most widely used entity categories: Person, Location, and Organization (Table 1).


This observation points to a serious weakness in the representativeness of existing datasets for the full complexity of the disambiguation task.

systems are not encouraged to focus on the temporal aspect of the task but in reality the same language system is still used for many different situations within a changing world.


Provided that this acquisition procedure is successful, selecting a dataset would require almost no effort, which enables creation of many, well-motivated datasets.

the dynamic nature of this process would challenge semantic overfitting.


Then we can interpret these relations as some kind of hierarchies.

just like dependent claim structures, we can rebuild structural elements in a claim into some hierarchical structures.


The term t o3 first appears in e 3 , then in e 4 .

parent(e 4 ) = e 3 , and ET (e 4 ) = t o3 .


But manual annotation needs domain knowledge of each technical field and expertise in reading patent claims.

preparing large amount of manual annotations for various technical fields is quite difficult.


We didn't find any published systems that conducted the same experiment to compare our results to.

table 4 only compares our results to the majority baseline and the binary model in table 3.


11 into simply computing the similarity between sentences, that is T ij i j e h h  .

the attention context for an answer derives from the similarity with the question as well as the associations with other answers.


A CRF layer is followed to capture the dependencies between adjacent labels via a state transition matrix.

equation 12 is transformed into a simpler form by replacing the potential functions by the outputs of Bi-LSTM.


Some of the measures did not yield a valid score for all the examples in our dataset.

the total number of semantic features is 62.


We evaluated the inter-annotator agreement and observed a Kappa (Cohen, 1960) value of 0.59, which is considered as moderate (Landis and Koch, 1977).

we considered a candidate as a VN-MWE or not only if both annotators agreed on its classification.


This might be because light verbs have little semantic content of their own and they are used in combination with various nouns.

the semantic similarity between the light verb and a specific noun was relatively low.


And the marked nominals of [company] e 1 and [publication] e 2 are not included in any context subsequence.

the sentence is divided into five parts: three context subsequences and two annotated nominals.


Particularly in tasks like relation classification, it is often impossible to determine the relation without the semantic ground of the target words.

previous approaches have been using lexical features like word embeddings, lemmas, WordNet, etc.


(2014) and 2015investigate tensor-based approaches for learning the combination of embedding features and lexicalized sparse features.

despite that relation classification has mostly been studied separately from SRL, they have a substantial amount of commonalities.


Furthermore, given the dependency parse tree, it's easy to find out that their associated predicate is "uses".

rC is expected to benefit both the identification and classification of semantic roles.